Paper: "Generate and Purify: Efficient Person Data Generation for Re-Identification" (accepted by IEEE Trans on Multimedia)
This temporary repository holds the codebase, data, and models for our paper.
.
├── data-generation-GAN # training and testing code for data generation
│ └── ...
├── data-purifying-GCN # training and testing code for data purifying
│ └── feature-extraction # extract features for affinity graph construction
│ └── ...
│ └── graph-clustering # link prediction and data purifying
│ └── ...
├── person-reid-baselines # training and testing code for person reid
│ └── ...
├── LICENSE
└── README.md
cd
to folder where you want to download this repo
Run git clone https://github.com/lulujianjie/efficient-person-generation-for-reid.git
Install dependencies:
Prepare dataset
9e34
, including market-pairs-train.csv, market-pairs-test.csv, market-annotation-train.csv, market-annotation-train.csv, duke-pairs-train.csv, duke-pairs-test.csv, duke-annotation-train.csv, duke-annotation-train.csvpython /data-generation-GAN/tool/generate_part_heatmap.py
Prepare pretrained models if you don't have
data-generation-GAN/config/cfg.py
and run
python data-generation-GAN/generate_samples_market.py
python data-generation-GAN/generate_samples_duke.py
data-purifying-GCN/feature-extraction/datasets/NewDataset.py
and modify the path of pre-trained model in data-purifying-GCN/feature-extraction/config/cfg.py
. Run
python data-purifying-GCN/feature-extraction/get_feats.py
cd data-purifying-GCN/graph-clustering/
and prepare data for GCN
python convert_npy_for_gcn.py
./config/cfg.py
and run
python test.py
python purify.py
cd .. && cd .. && cd person-reid-baselines
, modify the data path in main.py
of each baseline and run
python main.py
data-generation-GAN/config/cfg.py
python test.py
python tool/pytorch-fid/fid_score.py path/to/fake_imgs path/to/target_imgs
data-generation-GAN/config/cfg.py
, and run
python data-generation-GAN/train.py
data-purifying-GCN/graph-clustering/config/cfg.py
, and run
python graph-clustering/train.py
Please cite the following paper if you use this repository in your research. TBD
TBD
TBD